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Journal articles on the topic 'Soft classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Villandré, Luc, Benjamin Rich, and Antonio Ciampi. "Soft Classification Trees." Communications in Statistics - Theory and Methods 41, no. 16-17 (August 2012): 3244–58. http://dx.doi.org/10.1080/03610926.2011.632103.

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2

Sambu Seo, M. Bode, and K. Obermayer. "Soft nearest prototype classification." IEEE Transactions on Neural Networks 14, no. 2 (March 2003): 390–98. http://dx.doi.org/10.1109/tnn.2003.809407.

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3

Bonatz, Ekkehard, and Jorge E. Alonso. "Classification of Soft-Tissue Injuries." Techniques in Orthopaedics 10, no. 2 (1995): 73–78. http://dx.doi.org/10.1097/00013611-199501020-00003.

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4

Ibrahim, David A., Alan Swenson, Adam Sassoon, and Navin D. Fernando. "Classifications In Brief: The Tscherne Classification of Soft Tissue Injury." Clinical Orthopaedics and Related Research® 475, no. 2 (July 14, 2016): 560–64. http://dx.doi.org/10.1007/s11999-016-4980-3.

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5

Allison, Peter A. "Konservat-Lagerstätten:cause and classification." Paleobiology 14, no. 4 (1988): 331–44. http://dx.doi.org/10.1017/s0094837300012082.

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A review of the processes required for exceptional preservation of soft-bodied fossils demonstrates that anoxia does not significantly inhibit decay and emphasizes the importance of early diagenetic mineralization. Early diagenesis is the principal factor amongst the complex processes leading to soft-part preservation. The development of a particular preservational mineral is controlled by rate of burial, amount of organic detritus, and salinity. A new causative classification of soft-bodied fossil biotas is presented based upon fossil mineralogy and mineral paragenesis.

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6

Jaiswal, Tarun, Dr S. Jaiswal, and Dr Ragini Shukla. "Soft Computing Techniques Based Image Classification using Support Vector Machine Performance." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 1645–50. http://dx.doi.org/10.31142/ijtsrd23437.

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7

Mandal, Sudip, Goutam Saha, and Rajat K. Pal. "A Comparative Study on Disease Classification using Different Soft Computing Techniques." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 02, no. 04 (August 8, 2014): 22–29. http://dx.doi.org/10.9756/sijcsea/v2i4/0203110201.

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8

Mackay, Bruce. "Ultrastructural Classification of Soft Tissue Neoplasms." Ultrastructural Pathology 9, no. 3-4 (January 1985): 179. http://dx.doi.org/10.3109/01913128509074571.

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9

Gómez, D., G. Biging, and J. Montero. "Accuracy statistics for judging soft classification." International Journal of Remote Sensing 29, no. 3 (December 21, 2007): 693–709. http://dx.doi.org/10.1080/01431160701311325.

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10

Roverso, Davide. "Soft computing tools for transient classification." Information Sciences 127, no. 3-4 (August 2000): 137–56. http://dx.doi.org/10.1016/s0020-0255(00)00035-9.

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11

Popkov, Yuri, Zeev Volkovich, Yuri Dubnov, Renata Avros, and Elena Ravve. "Entropy “2”-Soft Classification of Objects." Entropy 19, no. 4 (April 20, 2017): 178. http://dx.doi.org/10.3390/e19040178.

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12

Srivastava, Pankaj, Neeraja Sharma, and C. S. Aparna. "Fuzzy Soft System and Arrhythmia Classification." Chinese Journal of Mathematics 2014 (February 18, 2014): 1–12. http://dx.doi.org/10.1155/2014/164781.

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An arrhythmia is an irregularity with the speed or rhythm of the heartbeat. During an arrhythmia, the heart can beat too fast, too slow, or with an irregular rhythm. Most arrhythmias are harmless, but some can be serious or even life threatening. The present paper deals with the classification scheme of arrhythmia commonly occurring in human beings of Southeast Asian countries. Medical knowledge used in practice has been closely studied for modelling user friendly referral system to sharpen arrhythmia diagnosis by experts and this system is tested with satisfactory factor which is measured with degree of match criterion under the domain of considered inputs and computed output.

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13

Gragnaniello, Diego, Giovanni Poggi, Giuseppe Scarpa, and Luisa Verdoliva. "SAR Image Despeckling by Soft Classification." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, no. 6 (June 2016): 2118–30. http://dx.doi.org/10.1109/jstars.2016.2561624.

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14

Frable, William J. "Pathologic classification of soft tissue sarcomas." Seminars in Surgical Oncology 10, no. 5 (September 1994): 332–39. http://dx.doi.org/10.1002/ssu.2980100505.

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15

Choudhuri, ManojKumar. "Cytology of soft tissue tumors: Cytological classification of soft tissue tumors." Journal of Cytology 25, no. 3 (2008): 79. http://dx.doi.org/10.4103/0970-9371.44033.

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16

Ke, Shih-Wen, Wei-Chao Lin, Chih-Fong Tsai, and Ya-Han Hu. "Soft estimation by hierarchical classification and regression." Neurocomputing 234 (April 2017): 27–37. http://dx.doi.org/10.1016/j.neucom.2016.12.037.

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17

Álvarez-Nemegyei, José, and Juan J. Canoso. "Name and Classification of Soft-Tissue Rheumatism." Reumatología Clínica (English Edition) 3, no. 4 (January 2007): 151–52. http://dx.doi.org/10.1016/s2173-5743(07)70236-8.

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18

van Unnik, J. A. M. "Classification and Grading of Soft-Tissue Sarcomas." Hematology/Oncology Clinics of North America 9, no. 3 (June 1995): 677–700. http://dx.doi.org/10.1016/s0889-8588(18)30091-1.

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19

Nguyen, Quang, Hamed Valizadegan, and Milos Hauskrecht. "Learning classification models with soft-label information." Journal of the American Medical Informatics Association 21, no. 3 (May 2014): 501–8. http://dx.doi.org/10.1136/amiajnl-2013-001964.

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20

OKUZAKI, Hidenori, and Yoshihito OSADA. "Classification and Research Trend of Soft Actuators." Journal of the Japan Society for Precision Engineering 80, no. 8 (2014): 709–12. http://dx.doi.org/10.2493/jjspe.80.709.

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21

Gilles, Frédéric, Vivianne Doussal, Annie Gentile, and Edmond Kahn. "TEXTURE IN CLASSIFICATION OF SOFT TISSUE TUMORS." Biology of the Cell 79, no. 3 (1993): 273. http://dx.doi.org/10.1016/0248-4900(93)90161-7.

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22

Inbarani, H. Hannah, S. Udhaya Kumar, Ahmad Taher Azar, and Aboul Ella Hassanien. "Hybrid rough-bijective soft set classification system." Neural Computing and Applications 29, no. 8 (November 28, 2016): 67–78. http://dx.doi.org/10.1007/s00521-016-2711-z.

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23

Aslam, Rubina, Manzoor Illahi Tamimy, and Waqar Aslam. "Soft Computing Based Evolutionary Multi-Label Classification." Intelligent Automation & Soft Computing 26, no. 4 (2020): 1233–49. http://dx.doi.org/10.32604/iasc.2020.013086.

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24

Li, Francis F. "Soft-Computing Audio Classification as a Pre-Processor for Automated Content Descriptor Generation." International Journal of Computer and Communication Engineering 3, no. 2 (2014): 101–4. http://dx.doi.org/10.7763/ijcce.2014.v3.300.

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25

Farsi, Carla. "SOFT C*-ALGEBRAS." Proceedings of the Edinburgh Mathematical Society 45, no. 1 (February 2002): 59–65. http://dx.doi.org/10.1017/s0013091500000547.

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AbstractIn this paper we consider soft group and crossed product $C^*$-algebras. In particular we show that soft crossed product $C^*$-algebras are isomorphic to classical crossed product $C^*$-algebras. We also prove that large classes of soft $C^*$-algebras have stable rank equal to infinity.AMS 2000 Mathematics subject classification: Primary 46L80; 46L55

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26

Halleluyah Oluwatobi, Aworinde, and Onifade O.F.W. "A Soft Computing Model of Soft Biometric Traits for Gender and Ethnicity Classification." International Journal of Engineering and Manufacturing 9, no. 2 (March 8, 2019): 54–63. http://dx.doi.org/10.5815/ijem.2019.02.05.

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27

Afonina, E. A., I. O. Golubev, and K. P. Pshenisnov. "New Functional Classification of Severe Hand Injuries (FU-classification)." N.N. Priorov Journal of Traumatology and Orthopedics 20, no. 1 (March 15, 2013): 3–9. http://dx.doi.org/10.17816/vto2013013-9.

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New functional classification of severe hand injuries is suggested. Concept of hand functional unit of the hand, i.e. finger segment containing joint, blood vessels and all surrounding soft tissues, is introduced. New functional classification of severe hand injuries is related to the algorithm of patient’s treatment depending on the level ofpreserved functional units. Division of the hand into functional units and primary evaluation of every unit condition enables to choose surgical treatment tactics directly after injury as well as to prognosticate the outcome.

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28

Hajdu, S. I. "Benign Soft Tissue Tumors: Classification and Natural History." CA: A Cancer Journal for Clinicians 37, no. 2 (March 1, 1987): 66–76. http://dx.doi.org/10.3322/canjclin.37.2.66.

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29

Shi, ZiQiang, JiQing Han, and TieRan Zheng. "Soft Margin Based Low-Rank Audio Signal Classification." Neural Processing Letters 42, no. 2 (May 14, 2014): 291–99. http://dx.doi.org/10.1007/s11063-014-9357-6.

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30

Gu, Jianyu, and Russell G. Congalton. "The Positional Effect in Soft Classification Accuracy Assessment." American Journal of Remote Sensing 7, no. 2 (2019): 50. http://dx.doi.org/10.11648/j.ajrs.20190702.13.

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31

Guest, Cameron, Edward H. M. Wang, Aileen Davis, Fred Langer, Brian OʼSullivan, Sabrena Noria, and Robert S. Bell. "Paraspinal Soft-Tissue Sarcoma Classification of 14 Cases." Spine 18, no. 10 (August 1993): 1292–97. http://dx.doi.org/10.1097/00007632-199308000-00008.

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32

Liu, Yufeng, Hao Helen Zhang, and Yichao Wu. "Hard or Soft Classification? Large-Margin Unified Machines." Journal of the American Statistical Association 106, no. 493 (March 2011): 166–77. http://dx.doi.org/10.1198/jasa.2011.tm10319.

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33

Fisher, C. "Soft tissue sarcomas: diagnosis, classification and prognostic factors." British Journal of Plastic Surgery 49, no. 1 (1996): 27–33. http://dx.doi.org/10.1016/s0007-1226(96)90183-6.

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34

Acuña, Sebastian, Ida S. Opstad, Fred Godtliebsen, Balpreet Singh Ahluwalia, and Krishna Agarwal. "Soft thresholding schemes for multiple signal classification algorithm." Optics Express 28, no. 23 (October 28, 2020): 34434. http://dx.doi.org/10.1364/oe.409363.

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35

Wadewitz, Philip, Kurt Hammerschmidt, Demian Battaglia, Annette Witt, Fred Wolf, and Julia Fischer. "Characterizing Vocal Repertoires—Hard vs. Soft Classification Approaches." PLOS ONE 10, no. 4 (April 27, 2015): e0125785. http://dx.doi.org/10.1371/journal.pone.0125785.

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36

Nilashi, Mehrbakhsh, Othman Bin Ibrahim, Abbas Mardani, Ali Ahani, and Ahmad Jusoh. "A soft computing approach for diabetes disease classification." Health Informatics Journal 24, no. 4 (November 14, 2016): 379–93. http://dx.doi.org/10.1177/1460458216675500.

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As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. The aim of this study is to classify diabetes disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noise removal and classification approaches. Accordingly, we use expectation maximization, principal component analysis and support vector machine for clustering, noise removal and classification tasks, respectively. We also develop the proposed method for incremental situation by applying the incremental principal component analysis and incremental support vector machine for incremental learning of data. Experimental results on Pima Indian Diabetes dataset show that proposed method remarkably improves the accuracy of prediction and reduces computation time in relation to the non-incremental approaches. The hybrid intelligent system can assist medical practitioners in the healthcare practice as a decision support system.

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37

Mankin, Henry J., and Francis J. Hornicek. "Diagnosis, Classification, and Management of Soft Tissue Sarcomas." Cancer Control 12, no. 1 (January 2005): 5–21. http://dx.doi.org/10.1177/107327480501200102.

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38

Dvořák, Jakub. "Classification trees with soft splits optimized for ranking." Computational Statistics 34, no. 2 (February 4, 2019): 763–86. http://dx.doi.org/10.1007/s00180-019-00867-1.

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39

Arnez, Z. M., U. Khan, and M. P. H. Tyler. "Classification of soft-tissue degloving in limb trauma." Journal of Plastic, Reconstructive & Aesthetic Surgery 63, no. 11 (November 2010): 1865–69. http://dx.doi.org/10.1016/j.bjps.2009.11.029.

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40

Read, S. A. L., and P. Millar. "Classification of New Zealand soft sedimentary rock materials." International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts 29, no. 4 (July 1992): 231. http://dx.doi.org/10.1016/0148-9062(92)90729-j.

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41

Phillips, Rhonda D., Layne T. Watson, David R. Easterling, and Randolph H. Wynne. "An SMP soft classification algorithm for remote sensing." Computers & Geosciences 68 (July 2014): 73–80. http://dx.doi.org/10.1016/j.cageo.2014.03.010.

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42

Tiwari, R. S., Manoj K. Arora, and T. Kailash. "Soft classification for sub-pixel land cover extraction." Journal of the Indian Society of Remote Sensing 27, no. 4 (December 1999): 225–34. http://dx.doi.org/10.1007/bf02990835.

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43

Kaur, Manvinder, Saravjeet Singh, and Dhanonjoy Shaw. "Advancements in soft computing methods for EMG classification." International Journal of Biomedical Engineering and Technology 20, no. 3 (2016): 253. http://dx.doi.org/10.1504/ijbet.2016.075428.

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44

Seo, Sambu, and Klaus Obermayer. "Soft Learning Vector Quantization." Neural Computation 15, no. 7 (July 1, 2003): 1589–604. http://dx.doi.org/10.1162/089976603321891819.

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Learning vector quantization (LVQ) is a popular class of adaptive nearest prototype classifiers for multiclass classification, but learning algorithms from this family have so far been proposed on heuristic grounds. Here, we take a more principled approach and derive two variants of LVQ using a gaussian mixture ansatz. We propose an objective function based on a likelihood ratio and derive a learning rule using gradient descent. The new approach provides a way to extend the algorithms of the LVQ family to different distance measure and allows for the design of “soft” LVQ algorithms. Benchmark results show that the new methods lead to better classification performance than LVQ 2.1. An additional benefit of the new method is that model assumptions are made explicit, so that the method can be adapted more easily to different kinds of problems.

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45

Luo, Jian, Shu-Cherng Fang, Zhibin Deng, and Xiaoling Guo. "Soft Quadratic Surface Support Vector Machine for Binary Classification." Asia-Pacific Journal of Operational Research 33, no. 06 (December 2016): 1650046. http://dx.doi.org/10.1142/s0217595916500469.

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In this paper, a kernel-free soft quadratic surface support vector machine model is proposed for binary classification directly using a quadratic function for separation. Properties (including the solvability, uniqueness and support vector representation of the optimal solution) of the proposed model are derived. Results of computational experiments on some artificial and real-world classifying data sets indicate that the proposed soft quadratic surface support vector machine model may outperform Dagher’s quadratic model and other soft support vector machine models with a Quadratic or Gaussian kernel in terms of the classification accuracy and robustness.

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46

He, Xin, and Yushi Chen. "Modifications of the Multi-Layer Perceptron for Hyperspectral Image Classification." Remote Sensing 13, no. 17 (September 6, 2021): 3547. http://dx.doi.org/10.3390/rs13173547.

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Recently, many convolutional neural network (CNN)-based methods have been proposed to tackle the classification task of hyperspectral images (HSI). In fact, CNN has become the de-facto standard for HSI classification. It seems that the traditional neural networks such as multi-layer perceptron (MLP) are not competitive for HSI classification. However, in this study, we try to prove that the MLP can achieve good classification performance of HSI if it is properly designed and improved. The proposed Modified-MLP for HSI classification contains two special parts: spectral–spatial feature mapping and spectral–spatial information mixing. Specifically, for spectral–spatial feature mapping, each input sample of HSI is divided into a sequence of 3D patches with fixed length and then a linear layer is used to map the 3D patches to spectral–spatial features. For spectral–spatial information mixing, all the spectral–spatial features within a single sample are feed into the solely MLP architecture to model the spectral–spatial information across patches for following HSI classification. Furthermore, to obtain the abundant spectral–spatial information with different scales, Multiscale-MLP is proposed to aggregate neighboring patches with multiscale shapes for acquiring abundant spectral–spatial information. In addition, the Soft-MLP is proposed to further enhance the classification performance by applying soft split operation, which flexibly capture the global relations of patches at different positions in the input HSI sample. Finally, label smoothing is introduced to mitigate the overfitting problem in the Soft-MLP (Soft-MLP-L), which greatly improves the classification performance of MLP-based method. The proposed Modified-MLP, Multiscale-MLP, Soft-MLP, and Soft-MLP-L are tested on the three widely used hyperspectral datasets. The proposed Soft-MLP-L leads to the highest OA, which outperforms CNN by 5.76%, 2.55%, and 2.5% on the Salinas, Pavia, and Indian Pines datasets, respectively. The obtained results reveal that the proposed models provide competitive results compared to the state-of-the-art methods, which shows that the MLP-based methods are still competitive for HSI classification.

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47

Kumar, S. Udhaya, H. Hannah Inbarani, Ahmad Taher Azar, and Aboul Ella Hassanien. "Identification of Heart Valve Disease using Bijective Soft Sets Theory." International Journal of Rough Sets and Data Analysis 1, no. 2 (July 2014): 1–14. http://dx.doi.org/10.4018/ijrsda.2014070101.

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Major complication of heart valve diseases is congestive heart valve failure. The heart is of essential significance to human beings. Auscultation with a stethoscope is considered as one of the techniques used in the analysis of heart diseases. Heart auscultation is a difficult task to determine the heart condition and requires some superior training of medical doctors. Therefore, the use of computerized techniques in the diagnosis of heart sounds may help the doctors in a clinical environment. Hence, in this study computer-aided heart sound diagnosis is performed to give support to doctors in decision making. In this study, a novel hybrid Rough-Bijective soft set is developed for the classification of heart valve diseases. A rough set (Quick Reduct) based feature selection technique is applied before classification for increasing the classification accuracy. The experimental results demonstrate that the overall classification accuracy offered by the employed Improved Bijective soft set approach (IBISOCLASS) provides higher accuracy compared with other classification techniques including hybrid Rough-Bijective soft set (RBISOCLASS), Bijective soft set (BISOCLASS), Decision table (DT), Naïve Bayes (NB) and J48.

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48

USUI, Hideharu, Kazuo SHIMOZATO, Ichiro OH-IWA, Shigeki OCHIAI, and Hideo FUKANO. "Proposal of a simple classification for maxillary hard-soft palate defects: 6-4 classification." Japanese Journal of Oral & Maxillofacial Surgery 52, no. 1 (2006): 2–6. http://dx.doi.org/10.5794/jjoms.52.2.

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49

Daszykowski, M., J. Orzel, M. S. Wrobel, H. Czarnik-Matusewicz, and B. Walczak. "Improvement of classification using robust soft classification rules for near-infrared reflectance spectral data." Chemometrics and Intelligent Laboratory Systems 109, no. 1 (November 2011): 86–93. http://dx.doi.org/10.1016/j.chemolab.2011.08.004.

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50

Zin, Nur Ariffin Mohd, Hishammuddin Asmuni, Haza Nuzly Abdul Hamed, Razib M. Othman, Shahreen Kasim, Rohayanti Hassan, Zalmiyah Zakaria, and Rosfuzah Roslan. "Contact Lens Classification by Using Segmented Lens Boundary Features." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 3 (September 1, 2018): 1129. http://dx.doi.org/10.11591/ijeecs.v11.i3.pp1129-1135.

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Recent studies have shown that the wearing of soft lens may lead to performance degradation with the increase of false reject rate. However, detecting the presence of soft lens is a non-trivial task as its texture that almost indiscernible. In this work, we proposed a classification method to identify the existence of soft lens in iris image. Our proposed method starts with segmenting the lens boundary on top of the sclera region. Then, the segmented boundary is used as features and extracted by local descriptors. These features are then trained and classified using Support Vector Machines. This method was tested on Notre Dame Cosmetic Contact Lens 2013 database. Experiment showed that the proposed method performed better than state of the art methods.

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